Link Prediction Researches Under Different Topology Structure Of Complex Network

The purpose of complex network link prediction is to find out the missing or potential links.It is widely used in the system of recommendation,crime prediction,group behavior discovery and network security.The method used to solve this problem is divided into two categories.One type of approach is based on the topology of the networks,which depends on the topology information of the networks.The advantage of this type of approach is fast,high efficiency.It is widely studied by researchers.Another type of approach is based on the classification of learning,which is usually require additional information(Such as age,hobbies,gender,etc.)and need to spend more time to train the model,so it is more difficult to meet the needs of real-time forecasting,but its predictive results are usually better than the topology-based approach.Most of these methods are used in smaller networks currently,and now many of the network data is growing exponentially every day,so,it is necessary to carry out link prediction research in large-scale network.According to the topological characteristics of the network,the network is divided into Tightly Connected within the Community?Hub Node within the Community?Sparse Connected between the Community and Tightly Connected between the Community,and analyze the feasible entry points of link prediction for these four types of networks respectively.In the HNWC network,the community information of the large-scale network is used to link prediction,and the density-based link clustering algorithm is used to divide each data into the community collection,which can ensure the characteristic of the stability of the community,fast and overlapping.The results show that the community information can effectively improve the performance of link prediction in large-scale network and reduce the time cost.Then,considering the influence of the neighbor in the HNWC network can improve the prediction performance to a certain extent.At the same time,The resource allocation(RA)is one of the better methods in the link prediction research based on topology.Therefore,on the basis of RA,the method of similarty with neighbors(SN)is presented by increase the impact of neighbors on the prediction results and then applied to predict large networks.The experimental results show that SN is very effective for HNWC network.In the TCWC and SCBC networks,the link prediction process is regarded as the process of network evolution and the evolution of the network is introduced because of the characteristics of nodes that tend to learn more than their own nodes,which is similar to the node strategy learning in game evolution.Based on this,we introduce the method of imitation learning in network evolution.In this method,we use the common neighbor(CN)to replace the yield difference,and allocate the link strength with RA.Finally,we propose a new method IL based on imitation learning.The experimental results show that IL has a better performance in the network with large clustering coefficient.